Overview

Dataset statistics

Number of variables21
Number of observations2000
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory328.2 KiB
Average record size in memory168.1 B

Variable types

Categorical11
Text4
Numeric6

Alerts

Year has constant value ""Constant
GeographicLevel has constant value ""Constant
DataSource has constant value ""Constant
Category has constant value ""Constant
Data_Value_Unit has constant value ""Constant
DataValueTypeID has constant value ""Constant
Data_Value_Type has constant value ""Constant
CategoryID has constant value ""Constant
Measure is uniformly distributedUniform
MeasureId is uniformly distributedUniform
Short_Question_Text is uniformly distributedUniform

Reproduction

Analysis started2024-04-18 17:51:39.279566
Analysis finished2024-04-18 17:51:45.839877
Duration6.56 seconds
Software versionydata-profiling vv4.7.0
Download configurationconfig.json

Variables

Year
Categorical

CONSTANT 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size15.8 KiB
2017
2000 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters8000
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2017
2nd row2017
3rd row2017
4th row2017
5th row2017

Common Values

ValueCountFrequency (%)
2017 2000
100.0%

Length

2024-04-18T12:51:45.976047image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-18T12:51:46.128933image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
2017 2000
100.0%

Most occurring characters

ValueCountFrequency (%)
2 2000
25.0%
0 2000
25.0%
1 2000
25.0%
7 2000
25.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 8000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 2000
25.0%
0 2000
25.0%
1 2000
25.0%
7 2000
25.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 8000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 2000
25.0%
0 2000
25.0%
1 2000
25.0%
7 2000
25.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 8000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 2000
25.0%
0 2000
25.0%
1 2000
25.0%
7 2000
25.0%
Distinct51
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Memory size15.8 KiB
2024-04-18T12:51:46.302971image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters4000
Distinct characters24
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCA
2nd rowCA
3rd rowCA
4th rowCA
5th rowCA
ValueCountFrequency (%)
ca 484
24.2%
tx 188
 
9.4%
fl 132
 
6.6%
il 72
 
3.6%
mi 64
 
3.2%
nc 56
 
2.8%
co 56
 
2.8%
wa 56
 
2.8%
ma 52
 
2.6%
az 48
 
2.4%
Other values (41) 792
39.6%
2024-04-18T12:51:46.652503image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 852
21.3%
C 652
16.3%
T 292
 
7.3%
N 280
 
7.0%
I 264
 
6.6%
L 252
 
6.3%
M 216
 
5.4%
X 188
 
4.7%
O 180
 
4.5%
F 132
 
3.3%
Other values (14) 692
17.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 852
21.3%
C 652
16.3%
T 292
 
7.3%
N 280
 
7.0%
I 264
 
6.6%
L 252
 
6.3%
M 216
 
5.4%
X 188
 
4.7%
O 180
 
4.5%
F 132
 
3.3%
Other values (14) 692
17.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 852
21.3%
C 652
16.3%
T 292
 
7.3%
N 280
 
7.0%
I 264
 
6.6%
L 252
 
6.3%
M 216
 
5.4%
X 188
 
4.7%
O 180
 
4.5%
F 132
 
3.3%
Other values (14) 692
17.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 852
21.3%
C 652
16.3%
T 292
 
7.3%
N 280
 
7.0%
I 264
 
6.6%
L 252
 
6.3%
M 216
 
5.4%
X 188
 
4.7%
O 180
 
4.5%
F 132
 
3.3%
Other values (14) 692
17.3%
Distinct51
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Memory size15.8 KiB
2024-04-18T12:51:46.886692image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length13
Median length11
Mean length8.44
Min length4

Characters and Unicode

Total characters16880
Distinct characters46
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCalifornia
2nd rowCalifornia
3rd rowCalifornia
4th rowCalifornia
5th rowCalifornia
ValueCountFrequency (%)
california 484
21.9%
texas 188
 
8.5%
florida 132
 
6.0%
new 96
 
4.3%
carolin 76
 
3.4%
illinois 72
 
3.3%
michigan 64
 
2.9%
north 60
 
2.7%
colorado 56
 
2.5%
washington 56
 
2.5%
Other values (45) 928
42.0%
2024-04-18T12:51:47.587481image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 2440
14.5%
i 2156
12.8%
o 1540
 
9.1%
n 1456
 
8.6%
r 1136
 
6.7%
l 996
 
5.9%
s 972
 
5.8%
e 764
 
4.5%
C 652
 
3.9%
f 488
 
2.9%
Other values (36) 4280
25.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 16880
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 2440
14.5%
i 2156
12.8%
o 1540
 
9.1%
n 1456
 
8.6%
r 1136
 
6.7%
l 996
 
5.9%
s 972
 
5.8%
e 764
 
4.5%
C 652
 
3.9%
f 488
 
2.9%
Other values (36) 4280
25.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 16880
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 2440
14.5%
i 2156
12.8%
o 1540
 
9.1%
n 1456
 
8.6%
r 1136
 
6.7%
l 996
 
5.9%
s 972
 
5.8%
e 764
 
4.5%
C 652
 
3.9%
f 488
 
2.9%
Other values (36) 4280
25.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 16880
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 2440
14.5%
i 2156
12.8%
o 1540
 
9.1%
n 1456
 
8.6%
r 1136
 
6.7%
l 996
 
5.9%
s 972
 
5.8%
e 764
 
4.5%
C 652
 
3.9%
f 488
 
2.9%
Other values (36) 4280
25.4%
Distinct474
Distinct (%)23.7%
Missing0
Missing (%)0.0%
Memory size15.8 KiB
2024-04-18T12:51:47.975352image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length26
Median length15
Mean length8.72
Min length4

Characters and Unicode

Total characters17440
Distinct characters56
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowHayward
2nd rowIndio
3rd rowBellflower
4th rowLynwood
5th rowRedding
ValueCountFrequency (%)
city 64
 
2.5%
san 44
 
1.7%
beach 40
 
1.6%
santa 32
 
1.3%
new 24
 
0.9%
st 24
 
0.9%
valley 20
 
0.8%
fort 20
 
0.8%
park 16
 
0.6%
west 16
 
0.6%
Other values (496) 2256
88.3%
2024-04-18T12:51:48.515936image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 1664
 
9.5%
e 1588
 
9.1%
n 1396
 
8.0%
o 1360
 
7.8%
l 1044
 
6.0%
r 1040
 
6.0%
i 1024
 
5.9%
t 896
 
5.1%
s 692
 
4.0%
556
 
3.2%
Other values (46) 6180
35.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 17440
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 1664
 
9.5%
e 1588
 
9.1%
n 1396
 
8.0%
o 1360
 
7.8%
l 1044
 
6.0%
r 1040
 
6.0%
i 1024
 
5.9%
t 896
 
5.1%
s 692
 
4.0%
556
 
3.2%
Other values (46) 6180
35.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 17440
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 1664
 
9.5%
e 1588
 
9.1%
n 1396
 
8.0%
o 1360
 
7.8%
l 1044
 
6.0%
r 1040
 
6.0%
i 1024
 
5.9%
t 896
 
5.1%
s 692
 
4.0%
556
 
3.2%
Other values (46) 6180
35.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 17440
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 1664
 
9.5%
e 1588
 
9.1%
n 1396
 
8.0%
o 1360
 
7.8%
l 1044
 
6.0%
r 1040
 
6.0%
i 1024
 
5.9%
t 896
 
5.1%
s 692
 
4.0%
556
 
3.2%
Other values (46) 6180
35.4%

GeographicLevel
Categorical

CONSTANT 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size15.8 KiB
City
2000 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters8000
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCity
2nd rowCity
3rd rowCity
4th rowCity
5th rowCity

Common Values

ValueCountFrequency (%)
City 2000
100.0%

Length

2024-04-18T12:51:48.716041image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-18T12:51:48.845372image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
city 2000
100.0%

Most occurring characters

ValueCountFrequency (%)
C 2000
25.0%
i 2000
25.0%
t 2000
25.0%
y 2000
25.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 8000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
C 2000
25.0%
i 2000
25.0%
t 2000
25.0%
y 2000
25.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 8000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
C 2000
25.0%
i 2000
25.0%
t 2000
25.0%
y 2000
25.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 8000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
C 2000
25.0%
i 2000
25.0%
t 2000
25.0%
y 2000
25.0%

DataSource
Categorical

CONSTANT 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size15.8 KiB
BRFSS
2000 

Length

Max length5
Median length5
Mean length5
Min length5

Characters and Unicode

Total characters10000
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowBRFSS
2nd rowBRFSS
3rd rowBRFSS
4th rowBRFSS
5th rowBRFSS

Common Values

ValueCountFrequency (%)
BRFSS 2000
100.0%

Length

2024-04-18T12:51:48.987562image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-18T12:51:49.113990image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
brfss 2000
100.0%

Most occurring characters

ValueCountFrequency (%)
S 4000
40.0%
B 2000
20.0%
R 2000
20.0%
F 2000
20.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 10000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
S 4000
40.0%
B 2000
20.0%
R 2000
20.0%
F 2000
20.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 10000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
S 4000
40.0%
B 2000
20.0%
R 2000
20.0%
F 2000
20.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 10000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
S 4000
40.0%
B 2000
20.0%
R 2000
20.0%
F 2000
20.0%

Category
Categorical

CONSTANT 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size15.8 KiB
Health Outcomes
2000 

Length

Max length15
Median length15
Mean length15
Min length15

Characters and Unicode

Total characters30000
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowHealth Outcomes
2nd rowHealth Outcomes
3rd rowHealth Outcomes
4th rowHealth Outcomes
5th rowHealth Outcomes

Common Values

ValueCountFrequency (%)
Health Outcomes 2000
100.0%

Length

2024-04-18T12:51:49.248088image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-18T12:51:49.375289image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
health 2000
50.0%
outcomes 2000
50.0%

Most occurring characters

ValueCountFrequency (%)
e 4000
13.3%
t 4000
13.3%
H 2000
 
6.7%
a 2000
 
6.7%
l 2000
 
6.7%
h 2000
 
6.7%
2000
 
6.7%
O 2000
 
6.7%
u 2000
 
6.7%
c 2000
 
6.7%
Other values (3) 6000
20.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 30000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 4000
13.3%
t 4000
13.3%
H 2000
 
6.7%
a 2000
 
6.7%
l 2000
 
6.7%
h 2000
 
6.7%
2000
 
6.7%
O 2000
 
6.7%
u 2000
 
6.7%
c 2000
 
6.7%
Other values (3) 6000
20.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 30000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 4000
13.3%
t 4000
13.3%
H 2000
 
6.7%
a 2000
 
6.7%
l 2000
 
6.7%
h 2000
 
6.7%
2000
 
6.7%
O 2000
 
6.7%
u 2000
 
6.7%
c 2000
 
6.7%
Other values (3) 6000
20.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 30000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 4000
13.3%
t 4000
13.3%
H 2000
 
6.7%
a 2000
 
6.7%
l 2000
 
6.7%
h 2000
 
6.7%
2000
 
6.7%
O 2000
 
6.7%
u 2000
 
6.7%
c 2000
 
6.7%
Other values (3) 6000
20.0%

UniqueID
Real number (ℝ)

Distinct500
Distinct (%)25.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2438986.3
Minimum15003
Maximum5613900
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.8 KiB
2024-04-18T12:51:49.539437image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum15003
5-th percentile598837.9
Q1670073.5
median1955965
Q34053125
95-th percentile5168421.6
Maximum5613900
Range5598897
Interquartile range (IQR)3383051.5

Descriptive statistics

Standard deviation1719002.8
Coefficient of variation (CV)0.70480217
Kurtosis-1.3868174
Mean2438986.3
Median Absolute Deviation (MAD)1314904
Skewness0.36425217
Sum4.8779726 × 109
Variance2.9549707 × 1012
MonotonicityNot monotonic
2024-04-18T12:51:49.731784image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
633000 4
 
0.2%
3436000 4
 
0.2%
3345140 4
 
0.2%
3474000 4
 
0.2%
3474630 4
 
0.2%
3350260 4
 
0.2%
3451000 4
 
0.2%
3457000 4
 
0.2%
3251800 4
 
0.2%
3413690 4
 
0.2%
Other values (490) 1960
98.0%
ValueCountFrequency (%)
15003 4
0.2%
107000 4
0.2%
135896 4
0.2%
137000 4
0.2%
150000 4
0.2%
151000 4
0.2%
177256 4
0.2%
203000 4
0.2%
404720 4
0.2%
412000 4
0.2%
ValueCountFrequency (%)
5613900 4
0.2%
5584250 4
0.2%
5566000 4
0.2%
5553000 4
0.2%
5548000 4
0.2%
5539225 4
0.2%
5531000 4
0.2%
5502375 4
0.2%
5414600 4
0.2%
5380010 4
0.2%

Measure
Categorical

UNIFORM 

Distinct4
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size15.8 KiB
Coronary heart disease among adults aged >=18 Years
500 
Chronic obstructive pulmonary disease among adults aged >=18 Years
500 
Cancer (excluding skin cancer) among adults aged >=18 Years
500 
Current asthma among adults aged >=18 Years
500 

Length

Max length66
Median length55
Mean length54.75
Min length43

Characters and Unicode

Total characters109500
Distinct characters30
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCoronary heart disease among adults aged >=18 Years
2nd rowChronic obstructive pulmonary disease among adults aged >=18 Years
3rd rowCoronary heart disease among adults aged >=18 Years
4th rowCancer (excluding skin cancer) among adults aged >=18 Years
5th rowCancer (excluding skin cancer) among adults aged >=18 Years

Common Values

ValueCountFrequency (%)
Coronary heart disease among adults aged >=18 Years 500
25.0%
Chronic obstructive pulmonary disease among adults aged >=18 Years 500
25.0%
Cancer (excluding skin cancer) among adults aged >=18 Years 500
25.0%
Current asthma among adults aged >=18 Years 500
25.0%

Length

2024-04-18T12:51:49.906106image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-18T12:51:50.054764image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
among 2000
12.1%
adults 2000
12.1%
aged 2000
12.1%
18 2000
12.1%
years 2000
12.1%
disease 1000
 
6.1%
cancer 1000
 
6.1%
coronary 500
 
3.0%
heart 500
 
3.0%
chronic 500
 
3.0%
Other values (6) 3000
18.2%

Most occurring characters

ValueCountFrequency (%)
14500
13.2%
a 12500
 
11.4%
e 9000
 
8.2%
s 7500
 
6.8%
r 7000
 
6.4%
n 6000
 
5.5%
d 5500
 
5.0%
t 4500
 
4.1%
o 4500
 
4.1%
g 4500
 
4.1%
Other values (20) 34000
31.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 109500
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
14500
13.2%
a 12500
 
11.4%
e 9000
 
8.2%
s 7500
 
6.8%
r 7000
 
6.4%
n 6000
 
5.5%
d 5500
 
5.0%
t 4500
 
4.1%
o 4500
 
4.1%
g 4500
 
4.1%
Other values (20) 34000
31.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 109500
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
14500
13.2%
a 12500
 
11.4%
e 9000
 
8.2%
s 7500
 
6.8%
r 7000
 
6.4%
n 6000
 
5.5%
d 5500
 
5.0%
t 4500
 
4.1%
o 4500
 
4.1%
g 4500
 
4.1%
Other values (20) 34000
31.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 109500
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
14500
13.2%
a 12500
 
11.4%
e 9000
 
8.2%
s 7500
 
6.8%
r 7000
 
6.4%
n 6000
 
5.5%
d 5500
 
5.0%
t 4500
 
4.1%
o 4500
 
4.1%
g 4500
 
4.1%
Other values (20) 34000
31.1%

Data_Value_Unit
Categorical

CONSTANT 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size15.8 KiB
%
2000 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2000
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row%
2nd row%
3rd row%
4th row%
5th row%

Common Values

ValueCountFrequency (%)
% 2000
100.0%

Length

2024-04-18T12:51:50.229161image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-18T12:51:50.356228image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
2000
100.0%

Most occurring characters

ValueCountFrequency (%)
% 2000
100.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
% 2000
100.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
% 2000
100.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
% 2000
100.0%

DataValueTypeID
Categorical

CONSTANT 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size15.8 KiB
AgeAdjPrv
2000 

Length

Max length9
Median length9
Mean length9
Min length9

Characters and Unicode

Total characters18000
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAgeAdjPrv
2nd rowAgeAdjPrv
3rd rowAgeAdjPrv
4th rowAgeAdjPrv
5th rowAgeAdjPrv

Common Values

ValueCountFrequency (%)
AgeAdjPrv 2000
100.0%

Length

2024-04-18T12:51:50.486982image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-18T12:51:50.614759image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
ageadjprv 2000
100.0%

Most occurring characters

ValueCountFrequency (%)
A 4000
22.2%
g 2000
11.1%
e 2000
11.1%
d 2000
11.1%
j 2000
11.1%
P 2000
11.1%
r 2000
11.1%
v 2000
11.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 18000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 4000
22.2%
g 2000
11.1%
e 2000
11.1%
d 2000
11.1%
j 2000
11.1%
P 2000
11.1%
r 2000
11.1%
v 2000
11.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 18000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 4000
22.2%
g 2000
11.1%
e 2000
11.1%
d 2000
11.1%
j 2000
11.1%
P 2000
11.1%
r 2000
11.1%
v 2000
11.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 18000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 4000
22.2%
g 2000
11.1%
e 2000
11.1%
d 2000
11.1%
j 2000
11.1%
P 2000
11.1%
r 2000
11.1%
v 2000
11.1%

Data_Value_Type
Categorical

CONSTANT 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size15.8 KiB
Age-adjusted prevalence
2000 

Length

Max length23
Median length23
Mean length23
Min length23

Characters and Unicode

Total characters46000
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAge-adjusted prevalence
2nd rowAge-adjusted prevalence
3rd rowAge-adjusted prevalence
4th rowAge-adjusted prevalence
5th rowAge-adjusted prevalence

Common Values

ValueCountFrequency (%)
Age-adjusted prevalence 2000
100.0%

Length

2024-04-18T12:51:50.747293image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-18T12:51:50.877795image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
age-adjusted 2000
50.0%
prevalence 2000
50.0%

Most occurring characters

ValueCountFrequency (%)
e 10000
21.7%
a 4000
 
8.7%
d 4000
 
8.7%
A 2000
 
4.3%
2000
 
4.3%
n 2000
 
4.3%
l 2000
 
4.3%
v 2000
 
4.3%
r 2000
 
4.3%
p 2000
 
4.3%
Other values (7) 14000
30.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 46000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 10000
21.7%
a 4000
 
8.7%
d 4000
 
8.7%
A 2000
 
4.3%
2000
 
4.3%
n 2000
 
4.3%
l 2000
 
4.3%
v 2000
 
4.3%
r 2000
 
4.3%
p 2000
 
4.3%
Other values (7) 14000
30.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 46000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 10000
21.7%
a 4000
 
8.7%
d 4000
 
8.7%
A 2000
 
4.3%
2000
 
4.3%
n 2000
 
4.3%
l 2000
 
4.3%
v 2000
 
4.3%
r 2000
 
4.3%
p 2000
 
4.3%
Other values (7) 14000
30.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 46000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 10000
21.7%
a 4000
 
8.7%
d 4000
 
8.7%
A 2000
 
4.3%
2000
 
4.3%
n 2000
 
4.3%
l 2000
 
4.3%
v 2000
 
4.3%
r 2000
 
4.3%
p 2000
 
4.3%
Other values (7) 14000
30.4%

Data_Value
Real number (ℝ)

Distinct96
Distinct (%)4.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.86405
Minimum3.1
Maximum14.2
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.8 KiB
2024-04-18T12:51:51.033481image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum3.1
5-th percentile4.4
Q15.6
median6.3
Q38.2
95-th percentile10.5
Maximum14.2
Range11.1
Interquartile range (IQR)2.6

Descriptive statistics

Standard deviation1.8998704
Coefficient of variation (CV)0.27678562
Kurtosis-0.11185704
Mean6.86405
Median Absolute Deviation (MAD)1.1
Skewness0.72640212
Sum13728.1
Variance3.6095074
MonotonicityNot monotonic
2024-04-18T12:51:51.233861image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6.2 92
 
4.6%
6.4 91
 
4.5%
6.1 87
 
4.3%
6.3 80
 
4.0%
6 72
 
3.6%
6.5 66
 
3.3%
5.9 65
 
3.2%
5.8 51
 
2.5%
5.7 48
 
2.4%
5.5 44
 
2.2%
Other values (86) 1304
65.2%
ValueCountFrequency (%)
3.1 3
 
0.1%
3.2 2
 
0.1%
3.3 2
 
0.1%
3.5 8
0.4%
3.6 7
0.4%
3.7 4
 
0.2%
3.8 7
0.4%
3.9 13
0.7%
4 10
0.5%
4.1 14
0.7%
ValueCountFrequency (%)
14.2 1
 
0.1%
13.1 1
 
0.1%
12.9 1
 
0.1%
12.8 1
 
0.1%
12.7 1
 
0.1%
12.6 2
0.1%
12.1 2
0.1%
12 2
0.1%
11.9 2
0.1%
11.8 3
0.1%

Low_Confidence_Limit
Real number (ℝ)

Distinct97
Distinct (%)4.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.75865
Minimum2.9
Maximum14.1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.8 KiB
2024-04-18T12:51:51.429597image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum2.9
5-th percentile4.2
Q15.5
median6.3
Q38.1
95-th percentile10.4
Maximum14.1
Range11.2
Interquartile range (IQR)2.6

Descriptive statistics

Standard deviation1.8918522
Coefficient of variation (CV)0.27991569
Kurtosis-0.094705069
Mean6.75865
Median Absolute Deviation (MAD)1.1
Skewness0.72020527
Sum13517.3
Variance3.5791047
MonotonicityNot monotonic
2024-04-18T12:51:51.633921image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6.3 95
 
4.8%
6.1 93
 
4.7%
6.2 87
 
4.3%
5.9 75
 
3.8%
6.4 73
 
3.6%
6 69
 
3.5%
5.8 68
 
3.4%
5.6 52
 
2.6%
6.5 47
 
2.4%
5.4 42
 
2.1%
Other values (87) 1299
65.0%
ValueCountFrequency (%)
2.9 1
 
0.1%
3 3
 
0.1%
3.1 1
 
0.1%
3.2 2
 
0.1%
3.3 2
 
0.1%
3.4 7
0.4%
3.5 4
0.2%
3.6 7
0.4%
3.7 8
0.4%
3.8 9
0.4%
ValueCountFrequency (%)
14.1 1
 
0.1%
13 1
 
0.1%
12.7 2
 
0.1%
12.5 2
 
0.1%
12.4 1
 
0.1%
12 2
 
0.1%
11.9 1
 
0.1%
11.8 4
0.2%
11.7 1
 
0.1%
11.6 5
0.2%

High_Confidence_Limit
Real number (ℝ)

Distinct96
Distinct (%)4.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.97325
Minimum3.2
Maximum14.3
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.8 KiB
2024-04-18T12:51:51.824131image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum3.2
5-th percentile4.5
Q15.7
median6.4
Q38.4
95-th percentile10.6
Maximum14.3
Range11.1
Interquartile range (IQR)2.7

Descriptive statistics

Standard deviation1.9087381
Coefficient of variation (CV)0.27372288
Kurtosis-0.13042103
Mean6.97325
Median Absolute Deviation (MAD)1.1
Skewness0.73321608
Sum13946.5
Variance3.6432811
MonotonicityNot monotonic
2024-04-18T12:51:52.017234image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6.2 90
 
4.5%
6.3 86
 
4.3%
6.4 82
 
4.1%
6.5 77
 
3.9%
6.1 77
 
3.9%
6 70
 
3.5%
5.9 59
 
2.9%
6.6 56
 
2.8%
5.8 47
 
2.4%
5.5 44
 
2.2%
Other values (86) 1312
65.6%
ValueCountFrequency (%)
3.2 3
 
0.1%
3.3 1
 
0.1%
3.4 2
 
0.1%
3.5 1
 
0.1%
3.6 8
0.4%
3.7 5
0.2%
3.8 7
0.4%
3.9 6
0.3%
4 12
0.6%
4.1 10
0.5%
ValueCountFrequency (%)
14.3 1
 
0.1%
13.2 1
 
0.1%
13.1 1
 
0.1%
13 1
 
0.1%
12.8 3
0.1%
12.2 3
0.1%
12.1 1
 
0.1%
12 3
0.1%
11.9 3
0.1%
11.8 5
0.2%

PopulationCount
Real number (ℝ)

Distinct497
Distinct (%)24.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean206041.62
Minimum42417
Maximum8175133
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.8 KiB
2024-04-18T12:51:52.210527image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum42417
5-th percentile67955.55
Q181590
median106106
Q3181127.75
95-th percentile601247.05
Maximum8175133
Range8132716
Interquartile range (IQR)99537.75

Descriptive statistics

Standard deviation457137.63
Coefficient of variation (CV)2.2186665
Kurtosis192.83124
Mean206041.62
Median Absolute Deviation (MAD)31015
Skewness12.271572
Sum4.1208323 × 108
Variance2.0897482 × 1011
MonotonicityNot monotonic
2024-04-18T12:51:52.426476image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
106433 8
 
0.4%
105549 8
 
0.4%
197899 8
 
0.4%
76780 4
 
0.2%
319294 4
 
0.2%
69781 4
 
0.2%
109565 4
 
0.2%
84913 4
 
0.2%
66455 4
 
0.2%
86494 4
 
0.2%
Other values (487) 1948
97.4%
ValueCountFrequency (%)
42417 4
0.2%
51400 4
0.2%
59466 4
0.2%
66135 4
0.2%
66154 4
0.2%
66194 4
0.2%
66455 4
0.2%
66588 4
0.2%
66702 4
0.2%
66748 4
0.2%
ValueCountFrequency (%)
8175133 4
0.2%
3792621 4
0.2%
2695598 4
0.2%
2099451 4
0.2%
1526006 4
0.2%
1445632 4
0.2%
1327407 4
0.2%
1307402 4
0.2%
1197816 4
0.2%
953207 4
0.2%
Distinct500
Distinct (%)25.0%
Missing0
Missing (%)0.0%
Memory size15.8 KiB
2024-04-18T12:51:52.714489image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length31
Median length31
Mean length30.786
Min length28

Characters and Unicode

Total characters61572
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row(37.6329591551, -122.077051051)
2nd row(33.7298067837, -116.237258141)
3rd row(33.8880417923, -118.127100236)
4th row(33.9239616867, -118.201648375)
5th row(40.5697591271, -122.365026322)
ValueCountFrequency (%)
37.6329591551 4
 
0.1%
86.8617007404 4
 
0.1%
41.3842399838 4
 
0.1%
81.728599153 4
 
0.1%
35.5959211876 4
 
0.1%
77.3766133673 4
 
0.1%
35.3939316958 4
 
0.1%
80.6352429349 4
 
0.1%
39.9361910066 4
 
0.1%
75.1072961899 4
 
0.1%
Other values (990) 3960
99.0%
2024-04-18T12:51:53.199673image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 5772
9.4%
3 5564
9.0%
2 4876
 
7.9%
8 4792
 
7.8%
7 4772
 
7.8%
4 4764
 
7.7%
6 4492
 
7.3%
9 4468
 
7.3%
5 4252
 
6.9%
. 4000
 
6.5%
Other values (6) 13820
22.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 61572
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 5772
9.4%
3 5564
9.0%
2 4876
 
7.9%
8 4792
 
7.8%
7 4772
 
7.8%
4 4764
 
7.7%
6 4492
 
7.3%
9 4468
 
7.3%
5 4252
 
6.9%
. 4000
 
6.5%
Other values (6) 13820
22.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 61572
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 5772
9.4%
3 5564
9.0%
2 4876
 
7.9%
8 4792
 
7.8%
7 4772
 
7.8%
4 4764
 
7.7%
6 4492
 
7.3%
9 4468
 
7.3%
5 4252
 
6.9%
. 4000
 
6.5%
Other values (6) 13820
22.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 61572
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 5772
9.4%
3 5564
9.0%
2 4876
 
7.9%
8 4792
 
7.8%
7 4772
 
7.8%
4 4764
 
7.7%
6 4492
 
7.3%
9 4468
 
7.3%
5 4252
 
6.9%
. 4000
 
6.5%
Other values (6) 13820
22.4%

CategoryID
Categorical

CONSTANT 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size15.8 KiB
HLTHOUT
2000 

Length

Max length7
Median length7
Mean length7
Min length7

Characters and Unicode

Total characters14000
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowHLTHOUT
2nd rowHLTHOUT
3rd rowHLTHOUT
4th rowHLTHOUT
5th rowHLTHOUT

Common Values

ValueCountFrequency (%)
HLTHOUT 2000
100.0%

Length

2024-04-18T12:51:53.403600image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-18T12:51:53.532095image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
hlthout 2000
100.0%

Most occurring characters

ValueCountFrequency (%)
H 4000
28.6%
T 4000
28.6%
L 2000
14.3%
O 2000
14.3%
U 2000
14.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 14000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
H 4000
28.6%
T 4000
28.6%
L 2000
14.3%
O 2000
14.3%
U 2000
14.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 14000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
H 4000
28.6%
T 4000
28.6%
L 2000
14.3%
O 2000
14.3%
U 2000
14.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 14000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
H 4000
28.6%
T 4000
28.6%
L 2000
14.3%
O 2000
14.3%
U 2000
14.3%

MeasureId
Categorical

UNIFORM 

Distinct4
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size15.8 KiB
CHD
500 
COPD
500 
CANCER
500 
CASTHMA
500 

Length

Max length7
Median length5
Mean length5
Min length3

Characters and Unicode

Total characters10000
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCHD
2nd rowCOPD
3rd rowCHD
4th rowCANCER
5th rowCANCER

Common Values

ValueCountFrequency (%)
CHD 500
25.0%
COPD 500
25.0%
CANCER 500
25.0%
CASTHMA 500
25.0%

Length

2024-04-18T12:51:53.687558image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-18T12:51:53.911048image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
chd 500
25.0%
copd 500
25.0%
cancer 500
25.0%
casthma 500
25.0%

Most occurring characters

ValueCountFrequency (%)
C 2500
25.0%
A 1500
15.0%
H 1000
 
10.0%
D 1000
 
10.0%
O 500
 
5.0%
P 500
 
5.0%
N 500
 
5.0%
E 500
 
5.0%
R 500
 
5.0%
S 500
 
5.0%
Other values (2) 1000
 
10.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 10000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
C 2500
25.0%
A 1500
15.0%
H 1000
 
10.0%
D 1000
 
10.0%
O 500
 
5.0%
P 500
 
5.0%
N 500
 
5.0%
E 500
 
5.0%
R 500
 
5.0%
S 500
 
5.0%
Other values (2) 1000
 
10.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 10000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
C 2500
25.0%
A 1500
15.0%
H 1000
 
10.0%
D 1000
 
10.0%
O 500
 
5.0%
P 500
 
5.0%
N 500
 
5.0%
E 500
 
5.0%
R 500
 
5.0%
S 500
 
5.0%
Other values (2) 1000
 
10.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 10000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
C 2500
25.0%
A 1500
15.0%
H 1000
 
10.0%
D 1000
 
10.0%
O 500
 
5.0%
P 500
 
5.0%
N 500
 
5.0%
E 500
 
5.0%
R 500
 
5.0%
S 500
 
5.0%
Other values (2) 1000
 
10.0%

CityFIPS
Real number (ℝ)

Distinct500
Distinct (%)25.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2438986.3
Minimum15003
Maximum5613900
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.8 KiB
2024-04-18T12:51:54.094296image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum15003
5-th percentile598837.9
Q1670073.5
median1955965
Q34053125
95-th percentile5168421.6
Maximum5613900
Range5598897
Interquartile range (IQR)3383051.5

Descriptive statistics

Standard deviation1719002.8
Coefficient of variation (CV)0.70480217
Kurtosis-1.3868174
Mean2438986.3
Median Absolute Deviation (MAD)1314904
Skewness0.36425217
Sum4.8779726 × 109
Variance2.9549707 × 1012
MonotonicityNot monotonic
2024-04-18T12:51:54.285613image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
633000 4
 
0.2%
3436000 4
 
0.2%
3345140 4
 
0.2%
3474000 4
 
0.2%
3474630 4
 
0.2%
3350260 4
 
0.2%
3451000 4
 
0.2%
3457000 4
 
0.2%
3251800 4
 
0.2%
3413690 4
 
0.2%
Other values (490) 1960
98.0%
ValueCountFrequency (%)
15003 4
0.2%
107000 4
0.2%
135896 4
0.2%
137000 4
0.2%
150000 4
0.2%
151000 4
0.2%
177256 4
0.2%
203000 4
0.2%
404720 4
0.2%
412000 4
0.2%
ValueCountFrequency (%)
5613900 4
0.2%
5584250 4
0.2%
5566000 4
0.2%
5553000 4
0.2%
5548000 4
0.2%
5539225 4
0.2%
5531000 4
0.2%
5502375 4
0.2%
5414600 4
0.2%
5380010 4
0.2%

Short_Question_Text
Categorical

UNIFORM 

Distinct4
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size15.8 KiB
Coronary Heart Disease
500 
COPD
500 
Cancer (except skin)
500 
Current Asthma
500 

Length

Max length22
Median length17
Mean length15
Min length4

Characters and Unicode

Total characters30000
Distinct characters25
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCoronary Heart Disease
2nd rowCOPD
3rd rowCoronary Heart Disease
4th rowCancer (except skin)
5th rowCancer (except skin)

Common Values

ValueCountFrequency (%)
Coronary Heart Disease 500
25.0%
COPD 500
25.0%
Cancer (except skin) 500
25.0%
Current Asthma 500
25.0%

Length

2024-04-18T12:51:54.475681image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-18T12:51:54.633886image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
coronary 500
11.1%
heart 500
11.1%
disease 500
11.1%
copd 500
11.1%
cancer 500
11.1%
except 500
11.1%
skin 500
11.1%
current 500
11.1%
asthma 500
11.1%

Most occurring characters

ValueCountFrequency (%)
e 3500
11.7%
r 3000
 
10.0%
2500
 
8.3%
a 2500
 
8.3%
C 2000
 
6.7%
t 2000
 
6.7%
s 2000
 
6.7%
n 2000
 
6.7%
D 1000
 
3.3%
i 1000
 
3.3%
Other values (15) 8500
28.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 30000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 3500
11.7%
r 3000
 
10.0%
2500
 
8.3%
a 2500
 
8.3%
C 2000
 
6.7%
t 2000
 
6.7%
s 2000
 
6.7%
n 2000
 
6.7%
D 1000
 
3.3%
i 1000
 
3.3%
Other values (15) 8500
28.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 30000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 3500
11.7%
r 3000
 
10.0%
2500
 
8.3%
a 2500
 
8.3%
C 2000
 
6.7%
t 2000
 
6.7%
s 2000
 
6.7%
n 2000
 
6.7%
D 1000
 
3.3%
i 1000
 
3.3%
Other values (15) 8500
28.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 30000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 3500
11.7%
r 3000
 
10.0%
2500
 
8.3%
a 2500
 
8.3%
C 2000
 
6.7%
t 2000
 
6.7%
s 2000
 
6.7%
n 2000
 
6.7%
D 1000
 
3.3%
i 1000
 
3.3%
Other values (15) 8500
28.3%

Interactions

2024-04-18T12:51:44.343999image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-18T12:51:40.094763image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-18T12:51:40.979908image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-18T12:51:41.838376image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-18T12:51:42.632099image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-18T12:51:43.429746image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-18T12:51:44.473737image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-18T12:51:40.266800image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-18T12:51:41.122321image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-18T12:51:41.971208image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-18T12:51:42.762994image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-18T12:51:43.570371image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-18T12:51:44.601370image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-18T12:51:40.414809image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-18T12:51:41.261103image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-18T12:51:42.094733image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-18T12:51:42.888322image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-18T12:51:43.706984image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-18T12:51:44.729554image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-18T12:51:40.552662image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-18T12:51:41.409402image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-18T12:51:42.226596image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-18T12:51:43.018267image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-18T12:51:43.916469image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-18T12:51:44.857039image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-18T12:51:40.685912image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-18T12:51:41.554425image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-18T12:51:42.355729image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-18T12:51:43.145564image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-18T12:51:44.053993image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-18T12:51:45.002396image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-18T12:51:40.841470image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-18T12:51:41.703434image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-18T12:51:42.503448image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-18T12:51:43.293368image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-18T12:51:44.206025image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Missing values

2024-04-18T12:51:45.227782image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-18T12:51:45.643071image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

YearStateAbbrStateDescCityNameGeographicLevelDataSourceCategoryUniqueIDMeasureData_Value_UnitDataValueTypeIDData_Value_TypeData_ValueLow_Confidence_LimitHigh_Confidence_LimitPopulationCountGeoLocationCategoryIDMeasureIdCityFIPSShort_Question_Text
02017CACaliforniaHaywardCityBRFSSHealth Outcomes633000Coronary heart disease among adults aged >=18 Years%AgeAdjPrvAge-adjusted prevalence4.84.74.8144186(37.6329591551, -122.077051051)HLTHOUTCHD633000Coronary Heart Disease
12017CACaliforniaIndioCityBRFSSHealth Outcomes636448Chronic obstructive pulmonary disease among adults aged >=18 Years%AgeAdjPrvAge-adjusted prevalence6.05.86.276036(33.7298067837, -116.237258141)HLTHOUTCOPD636448COPD
22017CACaliforniaBellflowerCityBRFSSHealth Outcomes604982Coronary heart disease among adults aged >=18 Years%AgeAdjPrvAge-adjusted prevalence5.35.25.476616(33.8880417923, -118.127100236)HLTHOUTCHD604982Coronary Heart Disease
32017CACaliforniaLynwoodCityBRFSSHealth Outcomes644574Cancer (excluding skin cancer) among adults aged >=18 Years%AgeAdjPrvAge-adjusted prevalence5.05.05.169772(33.9239616867, -118.201648375)HLTHOUTCANCER644574Cancer (except skin)
42017CACaliforniaReddingCityBRFSSHealth Outcomes659920Cancer (excluding skin cancer) among adults aged >=18 Years%AgeAdjPrvAge-adjusted prevalence6.66.66.789861(40.5697591271, -122.365026322)HLTHOUTCANCER659920Cancer (except skin)
52017FLFloridaCape CoralCityBRFSSHealth Outcomes1210275Coronary heart disease among adults aged >=18 Years%AgeAdjPrvAge-adjusted prevalence6.36.26.4154305(26.6448544993, -81.9931108298)HLTHOUTCHD1210275Coronary Heart Disease
62017FLFloridaPlantationCityBRFSSHealth Outcomes1257425Cancer (excluding skin cancer) among adults aged >=18 Years%AgeAdjPrvAge-adjusted prevalence6.05.96.184955(26.1259853923, -80.2616762465)HLTHOUTCANCER1257425Cancer (except skin)
72017CACaliforniaSanta ClaritaCityBRFSSHealth Outcomes669088Cancer (excluding skin cancer) among adults aged >=18 Years%AgeAdjPrvAge-adjusted prevalence6.16.16.1176320(34.4119039185, -118.503504698)HLTHOUTCANCER669088Cancer (except skin)
82017ILIllinoisNapervilleCityBRFSSHealth Outcomes1751622Chronic obstructive pulmonary disease among adults aged >=18 Years%AgeAdjPrvAge-adjusted prevalence4.14.04.3141853(41.7482001145, -88.1657705099)HLTHOUTCOPD1751622COPD
92017INIndianaLafayetteCityBRFSSHealth Outcomes1840788Current asthma among adults aged >=18 Years%AgeAdjPrvAge-adjusted prevalence10.310.110.467140(40.3994285285, -86.8617007404)HLTHOUTCASTHMA1840788Current Asthma
YearStateAbbrStateDescCityNameGeographicLevelDataSourceCategoryUniqueIDMeasureData_Value_UnitDataValueTypeIDData_Value_TypeData_ValueLow_Confidence_LimitHigh_Confidence_LimitPopulationCountGeoLocationCategoryIDMeasureIdCityFIPSShort_Question_Text
19902017WIWisconsinWaukeshaCityBRFSSHealth Outcomes5584250Chronic obstructive pulmonary disease among adults aged >=18 Years%AgeAdjPrvAge-adjusted prevalence4.84.65.070718(43.0093332215, -88.2457679157)HLTHOUTCOPD5584250COPD
19912017WIWisconsinWaukeshaCityBRFSSHealth Outcomes5584250Coronary heart disease among adults aged >=18 Years%AgeAdjPrvAge-adjusted prevalence4.94.85.070718(43.0093332215, -88.2457679157)HLTHOUTCHD5584250Coronary Heart Disease
19922017WYWyomingCheyenneCityBRFSSHealth Outcomes5613900Coronary heart disease among adults aged >=18 Years%AgeAdjPrvAge-adjusted prevalence5.25.15.459466(41.1460804265, -104.789064332)HLTHOUTCHD5613900Coronary Heart Disease
19932017WYWyomingCheyenneCityBRFSSHealth Outcomes5613900Cancer (excluding skin cancer) among adults aged >=18 Years%AgeAdjPrvAge-adjusted prevalence6.46.36.559466(41.1460804265, -104.789064332)HLTHOUTCANCER5613900Cancer (except skin)
19942017WIWisconsinWaukeshaCityBRFSSHealth Outcomes5584250Current asthma among adults aged >=18 Years%AgeAdjPrvAge-adjusted prevalence9.29.19.470718(43.0093332215, -88.2457679157)HLTHOUTCASTHMA5584250Current Asthma
19952017WIWisconsinRacineCityBRFSSHealth Outcomes5566000Chronic obstructive pulmonary disease among adults aged >=18 Years%AgeAdjPrvAge-adjusted prevalence6.36.16.578860(42.7274599494, -87.813453024)HLTHOUTCOPD5566000COPD
19962017WIWisconsinWaukeshaCityBRFSSHealth Outcomes5584250Cancer (excluding skin cancer) among adults aged >=18 Years%AgeAdjPrvAge-adjusted prevalence6.56.46.570718(43.0093332215, -88.2457679157)HLTHOUTCANCER5584250Cancer (except skin)
19972017WIWisconsinRacineCityBRFSSHealth Outcomes5566000Cancer (excluding skin cancer) among adults aged >=18 Years%AgeAdjPrvAge-adjusted prevalence6.26.26.378860(42.7274599494, -87.813453024)HLTHOUTCANCER5566000Cancer (except skin)
19982017WIWisconsinRacineCityBRFSSHealth Outcomes5566000Coronary heart disease among adults aged >=18 Years%AgeAdjPrvAge-adjusted prevalence6.05.96.178860(42.7274599494, -87.813453024)HLTHOUTCHD5566000Coronary Heart Disease
19992017WAWashingtonYakimaCityBRFSSHealth Outcomes5380010Cancer (excluding skin cancer) among adults aged >=18 Years%AgeAdjPrvAge-adjusted prevalence6.26.16.291067(46.5925792092, -120.547807614)HLTHOUTCANCER5380010Cancer (except skin)